Lung CT Synthesis Using GANs with Conditional Normalization on Registered Ultrashort Echo-Time MRI

In clinical practice, the modality of choice for lung diagnosis is usually computed tomography (CT), which exposes patients to ionizing radiations and could potentially affect patients’ health. Conversely, MR scan is considered safe and non-invasive but seems challenging due to the low proton density of the lungs and respiratory artifacts. Recently, ultrashort echo-time (UTE) MRI has been developed for lung assessment and shows promising results. In this work, we propose generating 2D synthetic CT slices from UTE MR slices, to improve the image quality and interpretability. Lung MR and CT volumes of 110 patients acquired on the same day were registered using an accurate edge-based non-rigid registration method. We trained and compared paired state-of-the-art generative models based on adversarial, feature-matching and perceptual losses, and also evaluated the impact of conditional batch normalization, namely SPADE [17], on image synthesis. Quantitative and qualitative evaluations showed that this approach was able to synthesize CT images that closely approximate ground truth CT images, and also enables the use of algorithms originally designed for real CT.